The present embodiments relate to segmentation in medical imaging. In particular, segmentation of heart vessels is provided.
Cardiac resynchronization therapy (CRT) is a challenging task for novice implanters and low-volume operators. Failure to implant a left ventricular (LV) lead is a common problem in CRT. This failure is often due to (1) the inability to cannulate the coronary sinus, (2) unfavorable venous anatomy resulting in the inability to find a stable lead position, or (3) failure to site the lead with acceptable pacing thresholds and without diaphragmatic pacing. Medical imaging may help to avoid improper lead placement. Precisely localizing the CS in imaging provides prior knowledge of coronary venous anatomy both in the selection of patients suitable for CRT and for the guidance of LV lead implantation.
Manual and semi-automatic segmentation methods facilitate identification and localization of coronary venous anatomy from three-dimensional (3D) whole-heart imaging acquisitions. Whole heart segmentation may be achieved automatically, but the coronary venous anatomy may have to be manually segmented by clinical experts. In semi-automatic approaches, defined start and end points of a vessel have to be given and labeled interactively. An automated approach uses a 3D anatomical model to extract cardiac chambers and large vessels, but extracts just a small part of the CS.
Automatic segmentation has been provided for coronary arteries for detecting coronary stenosis. Model-driven strategies utilize intensity based vesselness estimation approaches for extracting centerlines and segmenting coronary arteries. However, unlike coronary arteries, coronary venous vessels are commonly minute veins (e.g., with an even smaller radius and only a few voxels extended over the distal parts of the CS). Gross image inhomogeneity and poor contrast between regions of interest further complicate distinguishing the intensity variations of voxels inside and outside the CS vessel. Because of these significant challenges, automatic segmentation methods for extracting centerline or segmenting the CS vessel contour may fail to detect the coronary sinus accurately.